Neonatal EEG classification using atomic decomposition

dc.check.embargoformatE-thesis on CORA onlyen
dc.check.entireThesisEntire Thesis Restricted
dc.check.opt-outNoen
dc.check.reasonThis thesis is due for publication or the author is actively seeking to publish this materialen
dc.contributor.advisorLightbody, Gordonen
dc.contributor.advisorMarnane, William P.en
dc.contributor.advisorBoylan, Geraldine B.en
dc.contributor.advisorStevenson, Nathan J.en
dc.contributor.authorBelur Nagaraj, Sunil
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2015-12-02T10:26:32Z
dc.date.issued2015
dc.date.submitted2015
dc.description.abstractThe electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBelur Nagaraj, S. 2015. Neonatal EEG classification using atomic decomposition. PhD Thesis, University College Cork.en
dc.identifier.endpage173
dc.identifier.urihttps://hdl.handle.net/10468/2117
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2272/IE/Irish Centre for Fetal and Neonatal Translational Research (INFANT)/
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/10/IN.1/B3036/IE/Pattern RecognitIon Systems for continuous neurological Monitoring in NEOnates [NEOPRISM]./
dc.rights© 2015, Sunil Belur Nagaraj.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectNeonatal electroencephalogramen
dc.subjectAtomic decompositionen
dc.subjectSupport vector machineen
dc.thesis.opt-outfalse
dc.titleNeonatal EEG classification using atomic decompositionen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePHD (Engineering)en
ucc.workflow.supervisorg.lightbody@ucc.ie
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